Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
First, I'll compute the camera calibration using chessboard images¶
Importing the required libraries¶
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os
from moviepy.editor import VideoFileClip
%matplotlib qt
%matplotlib inline
Supporting functions¶
In [2]:
def print_two_images(img1, img2, title1, title2):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img1)
ax1.set_title(title1, fontsize=50)
ax2.imshow(img2, cmap='gray')
ax2.set_title(title2, fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
def read_image(file_path):
return mpimg.imread(file_path)
In [3]:
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
nx, ny = 6, 9
objp = np.zeros((nx*ny,3), np.float32)
objp[:,:2] = np.mgrid[0:ny,0:nx].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')
# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (ny,nx),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
else:
print("This image could not be used for callibration: " + fname)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
print("Amount of images used for calibration: " + str(len(images)))
print("Amount of sucessfull calibrations: " + str(len(objpoints)))
Applying a distortion correction to raw images¶
In [4]:
def undistort_image(img, mtx, dist):
return cv2.undistort(img, mtx, dist, None, mtx)
img1 = read_image("camera_cal/calibration5.jpg")
img2 = undistort_image(img1, mtx, dist)
print_two_images(img1, img2, "Original Image", "Undistorted Image")
Applying thresholding to an image¶
In [5]:
def apply_threshold(img):
# Convert to HLS color space and separate the S channel
# Note: img is the undistorted image
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
# Grayscale image
# NOTE: we already saw that standard grayscaling lost color information for the lane lines
# Explore gradients in other colors spaces / color channels to see what might work better
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Sobel x
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
thresh_min = 20
thresh_max = 100
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Threshold color channel
s_thresh_min = 170
s_thresh_max = 255
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh_min) & (s_channel <= s_thresh_max)] = 1
# Stack each channel to view their individual contributions in green and blue respectively
# This returns a stack of the two binary images, whose components you can see as different colors
color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary))
# Combine the two binary thresholds
combined_binary = np.zeros_like(sxbinary)
combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1
return combined_binary
filename = "test_images/straight_lines1.jpg"
img1 = read_image(filename)
img2= apply_threshold(img1)
print_two_images(img1, img2, "Original Image", "Binary Image")
Bird view¶
In [6]:
def apply_transform(image, draw_poly=False):
img_size = [image.shape[1], image.shape[0]]
src = np.float32(
[[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
[((img_size[0] / 6) - 10), img_size[1]],
[(img_size[0] * 5 / 6) + 60, img_size[1]],
[(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])
dst = np.float32(
[[(img_size[0] / 4), 0],
[(img_size[0] / 4), img_size[1]],
[(img_size[0] * 3 / 4), img_size[1]],
[(img_size[0] * 3 / 4), 0]])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(image, M, (image.shape[1],image.shape[0]), flags=cv2.INTER_LINEAR)
if draw_poly:
src_poly = np.array(((src[0][0], src[0][1]), (src[1][0], src[1][1]), (src[2][0], src[2][1]), (src[3][0], src[3][1])), np.int32)
cv2.polylines(image, [src_poly], True, (0,255,255),3)
dst_poly = np.array(((dst[0][0], dst[0][1]), (dst[1][0], dst[1][1]), (dst[2][0], dst[2][1]), (dst[3][0], dst[3][1])), np.int32)
cv2.polylines(warped, [dst_poly], True, (0,255,255),3)
return warped, cv2.getPerspectiveTransform(dst, src)
filename = "test_images/straight_lines1.jpg"
img1 = read_image(filename)
img2, Minv = apply_transform(img1, draw_poly=True)
print_two_images(img1, img2, "Original Image", "Bird View")
Detecting the lane lines for the first frame¶
In [7]:
def detect_lines_first_frame(binary_warped):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]/2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
return result, left_fit, right_fit, ploty
filename = "test_images/straight_lines1.jpg"
img1 = read_image(filename)
undistorted = undistort_image(img1, mtx, dist)
binarized = apply_threshold(undistorted)
bird_view, Minv = apply_transform(binarized)
detected_lines, left_fit, right_fit, ploty = detect_lines_first_frame(bird_view)
print_two_images(img1, detected_lines, "Original Image", "Detected lines")
Detecting follow up lines¶
In [8]:
def detect_lines_follow_up_frame(image, left_fit, right_fit):
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = image.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, image.shape[0]-1, image.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((image, image, image))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
return result, left_fit, right_fit, ploty, left_fitx, right_fitx
filename = "test_images/straight_lines2.jpg"
img1 = read_image(filename)
undistorted = undistort_image(img1, mtx, dist)
binarized = apply_threshold(undistorted)
bird_view, Minv = apply_transform(binarized)
detected_lines, left_fit, right_fit, ploty, left_fitx, right_fitx = detect_lines_follow_up_frame(bird_view, left_fit, right_fit)
print_two_images(img1, detected_lines, "Original Image", "Detected lines")
In [9]:
def project_line(original, warped, left_fitx, right_fitx, ploty, minv):
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (warped.shape[1], warped.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(original, 1, newwarp, 0.3, 0)
return result
filename = "test_images/straight_lines2.jpg"
img1 = read_image(filename)
undistorted = undistort_image(img1, mtx, dist)
binarized = apply_threshold(undistorted)
bird_view, Minv = apply_transform(binarized)
detected_lines, left_fit, right_fit, ploty, left_fitx, right_fitx = detect_lines_follow_up_frame(bird_view, left_fit, right_fit)
with_lines = project_line(img1, bird_view, left_fitx, right_fitx, ploty, Minv)
print_two_images(img1, with_lines, "Original", "Projected lines")
Complete pipeline¶
In [10]:
def detect_lines(input_image):
global left_fit
global right_fit
undistorted = undistort_image(input_image, mtx, dist)
binarized = apply_threshold(undistorted)
bird_view, Minv = apply_transform(binarized)
if len(left_fit) == 0 or len(right_fit) == 0:
detected_lines, left_fit, right_fit, ploty, left_fitx, right_fitx = detect_lines_first_frame(bird_view)
else:
detected_lines, left_fit, right_fit, ploty, left_fitx, right_fitx = detect_lines_follow_up_frame(bird_view, left_fit, right_fit)
with_lines = project_line(input_image, bird_view, left_fitx, right_fitx, ploty, Minv)
return with_lines
filename = "test_images/test5.jpg"
img1 = read_image(filename)
with_lines = detect_lines(img1)
print_two_images(img1, with_lines, "Original", "Projected lines")
Testing the pipeline on images¶
In [11]:
def process_test_image(image_name):
image = read_image("test_images/" + image_name)
processed_image = detect_lines(image)
mpimg.imsave("output_images/" + image_name, processed_image)
all_test_images = os.listdir("test_images")
for test_image in all_test_images:
process_test_image(test_image)
Testing the pipeline on a video¶
In [12]:
def process_test_video(video_name):
input_clip = VideoFileClip("test_videos/" + video_name)
output_clip = input_clip.fl_image(detect_lines)
%time output_clip.write_videofile("output_videos/" + video_name, audio=False)
process_test_video("project_video.mp4")